Patient Specific Computational Modeling in Cardiovascular Mechanics

  • Arthur Creane
  • Daniel J. Kelly
  • Caitríona Lally
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 5)


Diseases of the cardiovascular system are leading causes of morbidity and mortality worldwide. Computational modeling of cardiovascular mechanics has contributed to the understanding of cardiovascular disease etiology and risk evaluation. Patient specific finite element models of disease sites such as atherosclerotic plaques and aneurysms have provided important insights into their biomechanics, including identification of the characteristics of vulnerable locations.

Current clinical risk assessment for atherosclerotic plaque disruption is based on the stenosis produced by the lesion; however it has been found that the magnitude of stenosis does not correlate with the plaque’s vulnerability. Likewise evaluation of the likelihood of aneurysm rupture is based mainly on diameter measurements; however this criterion has also been called into question. Plaque and aneurysm rupture are often fatal events and thus improved clinical indicators for them are required. Patient specific finite element models of these disease sites may provide improved indicators of vulnerability based on biomechanical principles. Proposed indicators in the literature include measures of maximal stress and stress/strength ratios, additionally geometric measures such as plaque curvature or vessel asymmetry have also been developed as potential indicators.

In recent years, model complexity has increased from 2D studies to 3D models with multiple components. Current technical challenges which are being addressed in the literature include the estimation of the stress free reference configuration of arteries from the deformed in vivo configuration present in medical images and the inclusion of residual stresses in the arterial wall. Furthermore anisotropic constitutive models with artery specific preferred material directions are being implemented in these complex geometries using stress or strain based fiber remodeling algorithms and geometric systems. This chapter reviews the current state of the art in the area and details the barriers yet to be overcome if patient specific computational modeling is to be used as a clinical tool. These include trade-offs between automation, model complexity, computation time and reproducibility.


Residual Stress Abdominal Aortic Aneurysm Carotid Plaque Diffusion Tensor Magnetic Resonance Imaging Patient Specific Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Research Frontiers Grant 07/RFP/ENMF660 and grant 07/RFP/ENMF660 TIDA Feasibility 10.


  1. Alastrué V, Garía A, Peña E, Rodríguez JF, Martínez MA, Doblaré M (2010) Numerical framework for patient-specific computational modeling of vascular tissue. Int J Numer Methods Biomed Eng 26(1):35–51 zbMATHCrossRefGoogle Scholar
  2. Chen X, Schmitt F (1992) Intrinsic surface properties from surface triangulation. In: ECCV’92 proceedings of the second European conference on computer vision. Springer, Berlin, pp 739–743 Google Scholar
  3. Creane A, Maher E, Sultan S, Hynes N, Kelly DJ, Lally C (2010) Finite element modeling of diseased carotid bifurcations generated from in vivo computerised tomographic angiography. Comput Biol Med 40(4):419–429 CrossRefGoogle Scholar
  4. Creane A, Maher E, Sultan S, Hynes N, Kelly DJ, Lally C (2011) Prediction of fiber architecture and adaptation in diseased carotid bifurcations. Biomech Model Mechanobiol 10(6):831–843 CrossRefGoogle Scholar
  5. Delfino A, Stergiopulos N, Moore J, Meister JJ (1997) Residual strain effects on the stress field in a thick wall finite element model of the human carotid bifurcation. J Biomech 30(8):777–786 CrossRefGoogle Scholar
  6. Doyle BJ, Callanan A, Burke PE, Grace PA, Walsh MT, Vorp DA, McGloughlin TM (2009) Vessel asymmetry as an additional diagnostic tool in the assessment of abdominal aortic aneurysms. J Vasc Surg 49(2):443–454 CrossRefGoogle Scholar
  7. Driessen NJB, Wilson W, Bouten CVC, Baaijens FPT (2004) A computational model for collagen fiber remodeling in the arterial wall. J Theor Biol 226(1):53–64 CrossRefGoogle Scholar
  8. Fillinger MF, Marra SP, Raghavan ML, Kennedy FE (2003) Prediction of rupture risk in abdominal aortic aneurysm during observation: wall stress versus diameter. J Vasc Surg 37(4):724–732 CrossRefGoogle Scholar
  9. Fillinger MF, Raghavan ML, Marra SP, Cronenwett JL, Kennedy FE (2002) In vivo analysis of mechanical wall stress and abdominal aortic aneurysm rupture risk. J Vasc Surg 36(3):589–597 CrossRefGoogle Scholar
  10. Finlay HM, Whittaker P, Canham PB (1998) Collagen organization in the branching region of human brain arteries. Stroke 29(8):1595 CrossRefGoogle Scholar
  11. Flamini V, Kerskens C, Moerman KM, Simms CK, Lally C (2010) Imaging arterial fibers using diffusion tensor imaging—feasibility study and preliminary results. EURASIP J Adv Signal Process 2010:1–14 CrossRefGoogle Scholar
  12. Gao H, Long Q, Kumar Das S, Halls J, Graves M, Gillard JH, Li Z-Y (2011) Study of carotid arterial plaque stress for symptomatic and asymptomatic patients. J Biomech 44(14):2551–2557 CrossRefGoogle Scholar
  13. Gasser TC, Ogden RW, Holzapfel GA (2006) Hyperelastic modeling of arterial layers with distributed collagen fiber orientations. J R Soc Interface 3(6):15–35 CrossRefGoogle Scholar
  14. Gee MW, Förster C, Wall WA (2010) A computational strategy for prestressing patient-specific biomechanical problems under finite deformation. Int J Numer Methods Biomed Eng 26(1):52–72 zbMATHCrossRefGoogle Scholar
  15. Georgakarakos E, Ioannou CV, Kamarianakis Y, Papaharilaou Y, Kostas T, Manousaki E, Katsamouris AN (2010) The role of geometric parameters in the prediction of abdominal aortic aneurysm wall stress. Eur J Vasc Endovasc Surg 39(1):42–48 CrossRefGoogle Scholar
  16. Giannoglou G, Giannakoulas G, Soulis J, Chatzizisis Y, Perdikides T, Melas N, Parcharidis G, Louridas G (2006) Predicting the risk of rupture of abdominal aortic aneurysms by utilizing various geometrical parameters: revisiting the diameter criterion. Angiology 57(4):487–494 CrossRefGoogle Scholar
  17. Golledge J, Greenhalgh RM, Davies AH (2000) The symptomatic carotid plaque. Stroke 31(3):774–781 CrossRefGoogle Scholar
  18. Hameiri E, Shimshoni I (2003) Estimating the principal curvatures and the Darboux frame from real 3-d range data. IEEE Trans Syst Man Cybern, Part B, Cybern 33(4):626–637 CrossRefGoogle Scholar
  19. Hariton I, deBotton G, Gasser TC, Holzapfel GA (2007) Stress-modulated collagen fiber remodeling in a human carotid bifurcation. J Theor Biol 248(3):460–470 MathSciNetCrossRefGoogle Scholar
  20. Holzapfel GA, Gasser TC, Ogden RW (2000) A new constitutive framework for arterial wall mechanics and a comparative study of material models. J Elast 61(1):1–48 MathSciNetzbMATHCrossRefGoogle Scholar
  21. Huang X, Yang C, Yuan C, Liu F, Canton G, Zheng J, Woodard PK, Sicard GA, Tang D (2009) Patient-specific artery shrinkage and 3D zero-stress state in multi-component 3D FSI models for carotid atherosclerotic plaques based on in vivo MRI data. Mol Cell Biomech 6(2):121 Google Scholar
  22. Kiousis DE, Gasser TC, Holzapfel GA (2007) A numerical model to study the interaction of vascular stents with human atherosclerotic lesions. Ann Biomed Eng 35(11):1857–1869 CrossRefGoogle Scholar
  23. Leach JR, Rayz VL, Soares B, Wintermark M, Mofrad MRK, Saloner D (2010) Carotid atheroma rupture observed in vivo and FSI-predicted stress distribution based on pre-rupture imaging. Ann Biomed Eng 38(8):2748–2765 CrossRefGoogle Scholar
  24. Li Z-Y, Howarth SPS, Tang T, Graves MJ, U-King-Im J, Trivedi RA, Kirkpatrick PJ, Gillard JH (2007) Structural analysis and magnetic resonance imaging predict plaque vulnerability: a study comparing symptomatic and asymptomatic individuals. J Vasc Surg 45(4):768–775 CrossRefGoogle Scholar
  25. Li ZY, Tang T, U-King-Im J, Graves M, Sutcliffe M, Gillard JH (2008) Assessment of carotid plaque vulnerability using structural and geometrical determinants. Circ J 72(7):1092–1099 CrossRefGoogle Scholar
  26. Lu J, Zhou X, Raghavan ML (2007) Inverse elastostatic stress analysis in pre-deformed biological structures: demonstration using abdominal aortic aneurysms. J Biomech 40(3):693–696 CrossRefGoogle Scholar
  27. Ma B, Harbaugh RE, Raghavan ML (2004) Three-dimensional geometrical characterization of cerebral aneurysms. Ann Biomed Eng 32(2):264–273 CrossRefGoogle Scholar
  28. Maier A, Gee MW, Reeps C, Pongratz J, Eckstein HH, Wall WA (2010) A comparison of diameter, wall stress, and rupture potential index for abdominal aortic aneurysm rupture risk prediction. Ann Biomed Eng 38(10):3124–3134 CrossRefGoogle Scholar
  29. Martufi G, Di Martino ES, Amon CH, Muluk SC, Finol EA (2009) Three-dimensional geometrical characterization of abdominal aortic aneurysms: image-based wall thickness distribution. J Biomech Eng 131(6):061015 CrossRefGoogle Scholar
  30. Mortier P, Holzapfel GA, Beule M, Loo D, Taeymans Y, Segers P, Verdonck P, Verhegghe B (2009) A novel simulation strategy for stent insertion and deployment in curved coronary bifurcations: comparison of three drug-eluting stents. Ann Biomed Eng 38(1):88–99 CrossRefGoogle Scholar
  31. Naghavi M (2003) From vulnerable plaque to vulnerable patient: A call for new definitions and risk assessment strategies: part I. Circulation 108(14):1664–1672 CrossRefGoogle Scholar
  32. Ohayon J, Dubreuil O, Tracqui P, Le Floc’h S, Rioufol G, Chalabreysse L, Thivolet F, Pettigrew RI, Finet G (2007) Influence of residual stress/strain on the biomechanical stability of vulnerable coronary plaques: potential impact for evaluating the risk of plaque rupture. Am J Physiol, Heart Circ Physiol 293(3):H1987–H1996 CrossRefGoogle Scholar
  33. Pierce DM, Trobin W, Raya JG, Trattnig S, Bischof H, Glaser C, Holzapfel GA (2010) DT-MRI based computation of collagen fiber deformation in human articular cartilage: a feasibility study. Ann Biomed Eng 38(7):2447–2463 CrossRefGoogle Scholar
  34. Raghavan M, Ma B, Fillinger MF (2006) Non-invasive determination of zero-pressure geometry of arterial aneurysms. Ann Biomed Eng 34(9):1414–1419 CrossRefGoogle Scholar
  35. Rhodin JAG (1980) Architecture of the vessel wall. In: Bohr DF, Somlyo AD, Sparks HV (eds) The cardiovascular system. Handbook of physiology, vol 2. Am Physiol Soc, Bethesda, pp 1–31 Google Scholar
  36. Rissland P, Alemu Y, Einav S, Ricotta J, Bluestein D (2009) Abdominal aortic aneurysm risk of rupture: patient-specific FSI simulations using anisotropic model. J Biomech Eng 131(3):031001 CrossRefGoogle Scholar
  37. Rodríguez JF, Martufi G, Doblaré M, Finol EA (2009) The effect of material model formulation in the stress analysis of abdominal aortic aneurysms. Ann Biomed Eng 37(11):2218–2221 CrossRefGoogle Scholar
  38. Rodríguez JF, Ruiz C, Doblaré M, Holzapfel GA (2008) Mechanical stresses in abdominal aortic aneurysms: influence of diameter, asymmetry, and material anisotropy. J Biomech Eng 130(2):021023 CrossRefGoogle Scholar
  39. Rowe AJ, Finlay HM, Canham PB (2003) Collagen biomechanics in cerebral arteries and bifurcations assessed by polarizing microscopy. J Vasc Res 40(4):406–415 CrossRefGoogle Scholar
  40. Sacks MS, Vorp DA, Raghavan M, Federle MP, Webster MW (1999) In vivo three-dimensional surface geometry of abdominal aortic aneurysms. Ann Biomed Eng 27(4):469–479 CrossRefGoogle Scholar
  41. Sadat U, Teng Z, Young VE, Graves MJ, Gaunt ME, Gillard JH (2011) High-resolution magnetic resonance imaging-based biomechanical stress analysis of carotid atheroma: a comparison of single transient ischaemic attack, recurrent transient ischaemic attacks, non-disabling stroke and asymptomatic patient groups. Eur J Vasc Endovasc Surg 41(1):83–90 CrossRefGoogle Scholar
  42. Sadat U, Teng Z, Young VE, Walsh SR, Li ZY, Graves MJ, Varty K, Gillard JH (2010) Association between biomechanical structural stresses of atherosclerotic carotid plaques and subsequent ischaemic cerebrovascular events—a longitudinal in vivo magnetic resonance imaging-based finite element study. Eur J Vasc Endovasc Surg 40(4):485–491 CrossRefGoogle Scholar
  43. Schaar JA, Muller JE, Falk E, Virmani R, Fuster V, Serruys PW, Colombo A, Stefanadis C, Ward Casscells S, Moreno PR (2004) Terminology for high-risk and vulnerable coronary artery plaques. Eur Heart J 25(12):1077 CrossRefGoogle Scholar
  44. Shum J, DiMartino ES, Goldhammer A, Goldman DH, Acker LC, Patel G, Ng JH, Martufi G, Finol EA (2010a) Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms. Med Phys 37(2):638 CrossRefGoogle Scholar
  45. Shum J, Xu A, Chatnuntawech I, Finol EA (2010b) A framework for the automatic generation of surface topologies for abdominal aortic aneurysm models. Ann Biomed Eng 39(1):249–259 CrossRefGoogle Scholar
  46. Tang D, Teng Z, Canton G, Hatsukami TS, Dong L, Huang X, Yuan C (2009) Local critical stress correlates better than global maximum stress with plaque morphological features linked to atherosclerotic plaque vulnerability: an in vivo multi-patient study. Biomed Eng 8(1):15 Google Scholar
  47. Teng Z, Sadat U, Ji G, Zhu C, Young VE, Graves MJ, Gillard JH (2011) Lumen irregularity dominates the relationship between mechanical stress condition, fibrous-cap thickness, and lumen curvature in carotid atherosclerotic plaque. J Biomech Eng 133(3):034501 CrossRefGoogle Scholar
  48. Teng Z, Sadat U, Li Z, Huang X, Zhu C, Young VE, Graves MJ, Gillard JH (2010) Arterial luminal curvature and fibrous-cap thickness affect critical stress conditions within atherosclerotic plaque: an in vivo MRI-based 2D finite-element study. Ann Biomed Eng 38(10):3096–3101 CrossRefGoogle Scholar
  49. Vande Geest JP, Schmidt DE, Sacks MS, Vorp DA (2008) The effects of anisotropy on the stress analyses of patient-specific abdominal aortic aneurysms. Ann Biomed Eng 36(6):921–932 CrossRefGoogle Scholar
  50. Vande Geest JP, Wang DHJ, Wisniewski SR, Makaroun MS, Vorp DA (2006) Towards a noninvasive method for determination of patient-specific wall strength distribution in abdominal aortic aneurysms. Ann Biomed Eng 34(7):1098–1106 CrossRefGoogle Scholar
  51. Vorp DA (2007) Biomechanics of abdominal aortic aneurysm. J Biomech 40(9):1887–1902 CrossRefGoogle Scholar
  52. Vorp DA, Raghavan M, Webster MW (1998) Mechanical wall stress in abdominal aortic aneurysm: influence of diameter and asymmetry. J Vasc Surg 27(4):632–639 CrossRefGoogle Scholar
  53. Wu EX, Wu Y, Nicholls JM, Wang J, Liao S, Zhu S, Lau C-P, Tse H-F (2007) MR diffusion tensor imaging study of postinfarct myocardium structural remodeling in a porcine model. Magn Reson Med 58(4):687–695 CrossRefGoogle Scholar
  54. Yang C, Bach RG, Zheng J, Ei Naqa I, Woodard PK, Teng Z, Billiar K, Tang D (2009) In vivo IVUS-based 3-D fluid–structure interaction models with cyclic bending and anisotropic vessel properties for human atherosclerotic coronary plaque mechanical analysis. IEEE Trans Biomed Eng 56(10):2420–2428 CrossRefGoogle Scholar
  55. Zhang S, Crow JA, Yang X, Chen J, Borazjani A, Mullins KB, Chen W, Cooper RC, McLaughlin RM, Liao J (2010) The correlation of 3D DT-MRI fiber disruption with structural and mechanical degeneration in porcine myocardium. Ann Biomed Eng 38(10):3084–3095 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Arthur Creane
    • 1
  • Daniel J. Kelly
    • 2
  • Caitríona Lally
    • 1
    • 2
  1. 1.School of Mechanical and Manufacturing EngineeringDublin City UniversityDublin 9Ireland
  2. 2.Trinity Centre for Bioengineering, School of EngineeringTrinity CollegeDublin 2Ireland

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